Reconstructed Slides: Leadership in AI Assisted Engineering – Justin Reock, DX (acq. Atlassian)

Source Video

Leadership in AI Assisted Engineering – Justin Reock, DX (acq. Atlassian)

Method

This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

Reconstructed Slides

• Source frame: slide-001.jpg
• Crop: full [0, 0, 960, 540] score 176.59

• Source frame: slide-002.jpg
• Crop: contour [0, 0, 960, 540] score 177.24

• Source frame: slide-003.jpg
• Crop: contour [0, 0, 960, 540] score 177.2

• Source frame: slide-004.jpg
• Crop: full [0, 0, 960, 540] score 176.84

• Source frame: slide-005.jpg
• Crop: full [0, 0, 960, 540] score 166.51

• Source frame: slide-006.jpg
• Crop: full [0, 0, 960, 540] score 176.78

• Source frame: slide-007.jpg
• Crop: contour [0, 0, 960, 540] score 163.95

• Source frame: slide-008.jpg
• Crop: full [0, 0, 960, 540] score 165.11

• Source frame: slide-009.jpg
• Crop: full [0, 0, 960, 540] score 159.72

Dense Scene-Detected Slide Candidates

• youtube PmZDupFP3UM dense slides